A Fast Literature Search Engine based on top-quality journals, by Dr. Mingze Gao.
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- Please kindly let me know [mingze.gao@mq.edu.au] in case of any errors.
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Institutional investors expect infrastructure to deliver long-term stable returns but gain exposure to infrastructure predominantly through finite-horizon closed private funds. The cash flows delivered by infrastructure funds display similar volatility and cyclicality as other private equity investments, and their performance similarly depends on quick deal exits. Despite weak risk-adjusted performance and failure to match the supposed characteristics of infrastructure assets, closed funds have received more commitments over time, particularly from public investors. Public institutional investors perform worse than private institutional investors. ESG preferences and regulations explain 25–40 of their increased allocation to infrastructure and 30 of their underperformance.
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This paper shows the importance of correcting for sample selection when investing in illiquid assets that trade endogenously. Using a sample of 32,928 paintings that sold repeatedly between 1960 and 2013, we find an asymmetric V-shaped relation between sale probabilities and returns. Adjusting for the resulting selection bias reduces average annual index returns from 8.7% to 6.3%, lowers Sharpe ratios from 0.27 to 0.11, and materially impacts portfolio allocations. Investing in a broad portfolio of paintings is not attractive, but targeting specific styles or top-selling artists may add value. The methodology naturally extends to other asset classes.
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We estimate the risk and expected return of private equity using market prices of publicly traded funds of funds holding unlisted private equity funds and of publicly traded private equity funds participating directly in private equity transactions. We find that the market expects unlisted private equity funds to earn abnormal returns between −0.5% and 2% per year. In addition, private equity has a market beta close to one and a positive beta on the SMB factor. These listed funds exhibit greater systematic risk than an index based on the self-reported net asset value of unlisted private equity funds.
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We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale estimates are associated with substantially higher price‐to‐estimate ratios and lower buy‐in rates, pointing to estimates' informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers' prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
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We provide evidence that culture is a source of pricing bias. In a sample of 1.9 million auction transactions in 49 countries, paintings by female artists sell at an unconditional discount of 42.1%. The gender discount increases with measures of country-level gender inequality—even in artist fixed effects regressions. Our results are robust to accounting for potential gender differences in art characteristics and their liquidity. Evidence from two experiments supports the argument that women’s art may sell for less because it is made by women. However, the gender discount reduces over time as gender equality increases.
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